1. We know Opinion Land is a problem. CEOs aren’t seeing good solutions.
   

2. The fix is elusive because…

   1. We think we understand systems, when we don’t

   2. Humans regulate for safety - physical and psychological

   3. Opinion Land runs on a fear-based status game
      

3. The fix isn’t learning. It’s capability.

   1. Coaches in skill-based sports have already proven this

   2. There are only two fundamentals: problem solving and decision making
      

4. What you see when you get it right depends on who you are.

WE KNOW OPINION LAND IS A PROBLEM

CEOs ARE ASKING THE SAME QUESTION

CEOs are acknowledging they are in a tough spot, they’re looking for solutions.

1. Leadership is the #1 priority.

2. The current bench isn’t ready.

3. And hiring won’t fix it.

Transformation is a must in the AI era. But CEOs are saying the people expected to lead it don’t yet have the capability to do so.

Old playbook isn’t working. We need a new one.

THE FIX IS ELUSIVE (RECAP, 3 REASONS)

Reason 1 - Bicycle Problem is the Corporate Problem
(ie, this is the drag on productivity)

We think we know… until we have to explain it.

It means that we humans operate without an understanding of basic systems we depend on. It’s predictable. It’s pervasive. It creates a culture of confusion.

Reason 2. Humans are wired to seek safety, not clarity.
(ie, the “why” for this Substack)

Confusion is uncomfortable. Under pressure, it feels dangerous. And when short-term signals start flashing “something’s wrong,” a few billion years of evolutionary programming kicks in: Get OUT of there!

This isn’t weakness. Its biology.

In simple terms:

• Avoid pain = avoid destabilization

• Seek comfort = maintain stability

The earliest life forms solved this with one capability: monitor gradients that signal danger or stability.

That’s adaptive. And it still runs you. We didn’t lose this system, we extended it.

Human behavior is built on the same survival architecture and it operates with a more sophisticated version of the same capability: monitor signals that predict physical and psychological safety (or danger).

The implication?

Reading this post won’t change it.
Talking about this post won’t change it.
Deliberate skill acquisition will change it.

Without deliberate work on reasoning skills, we don’t solve problems, we manage safety.

That’s how Opinion Land starts.

Reason 3. Opinion Land is a powerful OS. It sucks.
(ie, how a culture of confusion hardens)

Opinion Land is the operating environment of most organizations.
Opinion Land emerges when biology meets pressure without capable reasoning.
Opinion Land trains people to survive the status game.

Pressure → Opinion → Status → Risk avoidance → Threat elimination

Repeat it enough and it becomes the system.

You don’t get clarity.
You get a system optimized for safety, not truth.

And left alone, it doesn’t fix itself. It scales.

That’s Opinion Land.

THE FIX TO OPINION LAND - CAPABILITY

The fix isn’t more learning. It’s capability. Not Coach #1. Not Coach #2. It’s Coach #3.

Learning is acquiring the skill.
Capability is performing it when it counts.

Learning assumes knowing leads to doing.
Capability requires doing under pressure.

THE DIFFERENCE BETWEEN LEARNING & CAPABILITY

WHAT CAPABILITIES PULL US OUT OF OPINION LAND?

That’s where I started.

In any skill-based sport, the first move is to anchor everything in fundamentals.

There are only two skill areas that matter:

How we solve problems.
How we make decisions.

There are only 4 ways to solve a problem.
There are only 4 kinds of decisions to make.

Tools and processes are utilized in those two skill areas.

If you develop capability in these, you eliminate the Bicycle Problem.

HOW WOULD YOU BUILD THOSE CAPABILITIES?

Here’s how sports coaching expert Howard Goldsmith (Talent Myth) would say it.

Master the 4 ways to solve a problem, and you can meet any situation.

Master the 4 types of decisions, and you can make the right call.

Here’s a checklist.

Does my development program…

• specify the skills required to solve problems and make decisions well?

• prove capability for each skill?

• use real-world reps?

• measure performance under real-world conditions, over time?

What happens when you get it right?

You move from a status game of opinions… to a game of progress.

Not occasionally. Consistently.

Problems get defined and solved correctly. Decisions get made with clarity.
Teams move forward because the work is grounded in reasoning, not noise.

WHAT YOU SEE WHEN YOU GET IT RIGHT DEPENDS ON WHO YOU ARE:

If you’re in L&D…
You stop delivering programs that feel like progress but break under pressure.
You start building capability that shows up in the work. Provable, repeatable, and durable. Engagement goes up. ROI goes up.

If you’re a CEO…
You finally have a logic for a Center of Excellence that works. Not another function. Not another tool. A system for building capability, whether AI is embedded or not. Your company breaks the tradeoff between speed and rigor.

If you’re an operator…
You manage the micro-shocks with a calm confidence that comes from competence. You’ll have the structure you need to solve any problem, or make any decision. You’ll improve your game, and up-level those around you.

If you’re an educator…
You will have a method for developing reasoning skills in the classroom. You move beyond teaching for understanding and into solving real problems and making real decisions. These are the skills of self-direction. This is what self-determination looks like in practice. Meet cognitive offloading head-on. Build real agency.

If you’re an investor…
You move beyond narrative explanations to predictable performance. Human readiness becomes the only lever that holds under pressure. Structured reasoning. At speed. With rigor. Improve it across the portfolio, and the gains compound.

CONCLUSION:

This isn’t about better thinking.

It’s about reliable reasoning under real conditions.

That’s what breaks Opinion Land.

That’s what creates progress.

In the next posts, I’ll break down…

• the four ways to solve a problem

• the four types of decisions

• curate partners,

• translate between them,

• integrate disparate intentions,

• develop EQ, and

• know the context.

The premise - if you don’t have bridgers, your innovations won’t scale.

Cool. At least that’s what I thought the first time I read it, 20 years ago.

Look at this list.

40-YEAR SUMMARY: align people, use EQ, build trust, translate across groups, surface assumptions, understand context, collaborate across silos.

What’s the pattern?

1. Why are we talking about the same “best practices” as if they’re new?

2. Why are we talking about the same prescriptions as if they will work this time around?

   ---

The questions aren’t to criticize the authors,
but to provoke us all.

why aren’t we integrating best practices?

You can take aspirin for the headache, but if you don’t stop the hammer causing it, the pain will persist.

I met Peter Senge when we both worked for a common client. He’s the OG of organizational change. He had EQ, and the 5th disciple is incredibly relevant today. But it fell out of favor. Why?

It didn’t really work.

So what?

Transformational AI, and HBR’s Innovation article, feels just like the change prescriptions of yesterday.

Lets decode it.

The operator’s thought exercise:

• You know your company needs to transform and you ask a consultant for help. (AI and Innovation transformation are interchangeable here).

• The consultants title their scope as, “Transformation for the [AI or Scale Innovation] world.”

• It’s sold as the answer to the organizational pain, “We have your change management playbook.”

• This is the path to survival!

Then you ask…

1. Why will change management work this time? (When it didn’t really deliver the results predicted before.)

2. What have you improved over Senge’s stuff? (What is actually different?)

What’s broken here?

Companies display symptoms
↓
experts observe them
↓
companies seek relief
↓
experts prescribe observable behaviors of good companies.

Explanation ≠ Prediction

Maybe the prescriptions aren’t dealing directly with the cause of the headache.

The hammer.

The Hammer

Innovation doesn’t fail because organizations lack “bridgers.”

Innovation fails…

because DECISIONS inside organizations

are governed by playbooks of OPINION LAND
(offense and defense)

rather than structured
PROBLEM SOLVING and DECISION LOGIC

But if we are using best practices, how can I say that we’re not using good problem solving and decision logic?

Innovation fails when decisions are governed by Opinion Land. I know, I was there.

In the mid-2000’s I represented my company at the Innovation Round Table. These were the large and cutting edge companies of northern Europe who spoke at conferences. They had collaborations with thought leaders like Clayton Christensen (Disruption!), Henry Chesbrough (Open Innovation!), Tim Brown (Design Thinking!), Jane McGonigal (Gamification!). They had wins to point at. Even the Lego Group turned its struggling toy business around by expanding the brand into storytelling and entertainment. (Movies!) Yeah man, this was cool!

Until you talked to innovation leaders privately.

Behind the scenes the story was very different. Not 100% of the time, but often enough to notice a pattern: good ideas were there, corporations kill good ideas, politics and power struggles dominate how things get done, and it makes no sense. Innovation leaders were frustrated!

Then 20 minutes later you’d see the same person, on stage, talking about how amazing their innovation program was.

Innovation fails for the same reason working in Opinion Land sucks: innovation is about what’s new, that’s risky, and that’s a threat. Opinion Land leaders are conditioned to eliminate threats. They’re leaders because they’re good at it.

So what’s the actual hammer? Our cognitive pre-sets.

Individual Hard wiring. We don’t know what we think we know. Those pre-sets drive the threat reflexes that emerge within a culture of confusion. It’s the power dynamics of the status game. That’s Opinion Land. It sucks.

Innovation is difficult because the human nervous system is designed to avoid destabilization — and innovation is destabilization.

This dynamic is embodied. It’s why I call it the Human Embodiment Problem.

“Give it up already! This is human nature, you can’t do anything about it.”

I disagree.

FIRST - THE OLD LOGIC WON’T WORK, so lets stop assuming the expert prescriptions – more of the same – will give us the sustainable relief we’re looking for, i.e., Same failed strategy will give a different result. That’s a definition of crazy. That’s why I decoded the HBR article.

Explanatory solutions framed as best practices are cool, they feel right, but treating symptoms ≠ relief.

SECOND - THE NEW LOGIC MUST TIE TO PHYSIOLOGY, so lets strip out the word “culture”, that’s a consequence of collective behavior. The collective behavior is a roll up individual and team behavior.

Predictive solutions framed as the work of developing human capacity for structured responses to high pressure conditions = relief.

But there’s a learning curve.

Summarizing this post: decoding patterns we see.

Innovation introduces instability.
Human physiology is wired to defend stability.
That tension explains innovation and transformation failures.

The next post: the answer for the hammer problem.

How do we intervene on an ancient physiological reflex to avoid destabilization? How do organizations do that? It’s simple, actionable, and has a LOT of precedence. Coach #3 points the way.

Stay tuned.

Game On.

Yesterday I heard an executive describe his company’s AI strategy.

“We’re not going to train you guys on AI.
We’re embedding AI experts inside each department.”

Finance gets one. Marketing gets one. Operations gets one. Etc.

The idea is simple. Instead of waiting for thousands of employees to learn AI tools, you bring someone in who already knows how to use them, they sit next to the leader, and roll out solutions. Analysis. Automation. Agents. Reports. Workflows.

Real output. Real productivity. Fast!

This approach is gaining traction. Consulting firms talk about:

• AI translators
• fusion teams
• hub-and-spoke AI operating models
• embedded AI pods

The logic is compelling.

Challenge: AI tools are powerful, but most leaders don’t know how to use them effectively.

Solution: Instead of training everyone, you embed capability directly where decisions are made.

Give the executive a coach. An AI coach. Let’s call them Coach #4.

Feels smart. About as smart as building an expensive building on sand.

At first glance, Coach #4 sounds like progress.

It feels like action, but runs with risk that’s hard to see.

I believe it will produce real work for many firms. Work gets done faster. Experiments happen sooner. Ideas turn into outputs. And right now, organizations feel enormous pressure to capture the productivity gains promised by AI.

Pressure is mounting to move, and internal leaders sit at the intersection of compounding confusion and the pressure to act.

A classic pattern emerges:

• you go with with what experts tell you (consulting industry)

• you grab at new offerings (tech and tools)

• you follow what peer organizations do (trends)

Embedding AI experts checks all three boxes.

To see it, we need appreciate the Human Embodiment Problem, and then revisit the last post. In sports, great coaches focus on fundamentals.

Ball control. Positioning. Passing. They know that advanced plays only work when the underlying techniques are solid.

That’s why Coach #3 — the Quality Reps Coach — focuses on deliberate practice. When pressure hits in a real match, players don’t rise to theory. They fall to their training. This is common knowledge, but do we apply it in business? If we do, it’s underachieving expectations, at least that’s what CEOs are saying.

Besides AI saavy, what does (AI) Coach #4 need to bring?

The two that matter more than anything else:

problem solving

and

decision making

Imagine introducing powerful AI tools into an environment where these two fundamentals are weak?

AI systems require something very specific to work well. Three things must be clear:

• the problem
• the decision criteria
• the measure of success

Without those inputs, AI still produces output. Lots of output. But output isn’t the same as progress. Which leads to two simple equations –  one you want, one you don’t.

AI × opinion = faster confusion

Instead of:

AI × structured reasoning = progress

This is the multiplier problem. AI accelerates whatever thinking system already exists inside the organization. If teams operate in structured reasoning environments, AI becomes a powerful amplifier. If teams operate in Opinion Land, AI amplifies the noise. Faster analysis. Faster narratives. Faster justification. But not necessarily faster problem resolution or better decisions.

This doesn’t mean the embedded AI strategy is wrong. It simply means it’s incomplete. Embedded AI experts can accelerate the reps. But if the fundamentals aren’t there, the reps reinforce bad technique.

The deeper issue is something most AI strategies ignore.

Problem solving and decision making are the data architecture for human thinking.

In the face of accelerating change, the most durable lever companies have is the human capacity to solve problems and make decisions effectively.

That’s the foundation. Without those fundamentals, the tools will fall short of expectations.

And the next wave is already forming. Companies won’t just embed AI experts. Leaders will be managing a team of AI agents, or AI workers. Executing tasks, running workflows, and producing work alongside humans. Which raises an interesting question.

If leaders struggle to frame problems for humans…what happens when they have to frame them for machines?

AI is a multiplier.

The real question is: multiplier of what?

So now what?

1. “I get the premise. I see the consequences every day - there’s a dissonance between what we say at work, and what we do at work. It doesn’t feel good, it doesn’t feel right.”

2. “Theories about the Future of Work aren’t enough, I need more than value statements, personality assessments, tips for leading remote teams, unifying BHAG’s, employee survey reviews, and calls for me to be more of a coach.”

3. “What I want is a playbook that describes the fundamentals I need to master to develop my leaders’ effectiveness in creating an engaged workforce. I’ve seen the change initiatives and heard countless calls for transformation, but the tools we use seem to be mismatched - they don’t work, at least not well enough. It feels stale. I don’t trust it’ll be different, but I’d like it to be.”

This post picks up on the notion that skill acquisition is a human process, even at work. Someone asked me to explain what I meant by this, I shared this story.

Imagine you’re a parent and your child asks, “Mom, Dad, I want to be a great soccer player, can you help me find a good coach?”

This request kicks off a straight forward set of actions: ask around, get names, meet with coaches, review the options, make a choice. “Yes of course, we can!”

Master coaching is like gold, you’re eager to find it. You get a short list of three coaches to meet with. “Can I come too,” your child asks. “Absolutely, it’s your sport, we want you to be excited about the coach, you have to meet with them!” You load into the car, and drive to the first of three meetings you’ve arranged.

Coach #1: When you meet the first coach at the athletic facility, he greets you with enthusiasm and excitement for his program. “Great to meet you, we have a smart program here!” He pulls out a box full of books and binders, and hands it to the aspiring athlete, but it’s awkward and too heavy. Your little soccer player drops it. “That’s ok, you’ll handle that in no time,” says Coach #1, “What I want you to do is read the books on fitness, training philosophy, and nutrition! Then memorize the binder material, that’s our playbook. Be disciplined on this, it’s not enough to read it, you have to know this material, and we have metrics we use to evaluate how well you do! Master all of this content this week, because we have a game on Saturday. Know the stuff in this box, and you’ll do great.” Coach #1 is the Knowledge Coach.

Coach #2: Next, you meet the second coach at his athletic facility. He greets you with enthusiasm and excitement for his program. “Great to meet you, we have really experienced coaching here!” He pulls out a single piece of paper and hands it to the little soccer player. “That’s our schedule for the week. Monday thru Friday, right in this room, two hours a day. I’ll tell you everything you need to know: the fundamentals, the positions, roles and responsibilities, rules of the game, and our game plan - real X’s and O’x stuff! You’ll need to master everything by Saturday, because we have a game. Don’t worry, I’ll tell you what you need to do. You’ll do great!” Coach #2 is the Talking Coach.

Coach #3 When you meet the third coach, he greets you with enthusiasm and excitement for your interest. “Great to meet you, we have a great group of athletes, would you like to meet them after I answer your questions?” Your aspiring player is shy, but nods. “Great! So, I promise to keep this part short then.” Coach #3 speaks calmly. “In summary, it’s simple: There are a few key concepts you’ll need to know about the game and your role in it, but we explain this when you’re on the field. You’ll get right into the fundamentals that matter most: kicking, ball control, situational rehearsal. Fitness is built into the drills. Since you’re just starting out, a lot of this will be new, so it might feel awkward or uncomfortable, and that’s normal, that’s true for everyone. Also, I hope you’re available this weekend, we have a game on Saturday.” You’re cautious, and ask, “Isn’t that too soon to be in a game?” He answers with a smile, “It’s too soon to expect more than where they are in this phase, that’s for sure.” To test further, you follow up, “Well then what does success look like?” Coach #3 points to the poster on the wall, The Competencies of The Game. “Those are the areas of work, each area of work has skills. Each skill has techniques we drill. How to train is important around here. Quality reps on the field is my metric of success. I know that deep practice on these techniques is the only way to make progress, and progress is what kids need to see to stay motivated. So we turn these drills into games, where each player can track their own improvement. It’s fun! They’re always competing against their last best performance. I learn from this too. When I observe match-level instincts, if the kids don’t produce the right technique, it’s one of two things: 1) It’s phase appropriate, there’s nothing to change, they’re developing, or 2) It’s a gap in my coaching and I need to adjust the drills, volume, or intensity. So that’s on me, not them. I don’t worry about wins and losses, I’m more focused on connecting their effort to the progress they can make. The rest falls into place. It’s a learning process, for all of us. Whadyasay, shall we head down to the field?” Coach #3 is the Quality Reps Coach.

What’s the point? Here’s the logic: The job of leadership is defined by moments of situational performance, small interactions, just like a dynamic soccer game. It’s fast and fluid. For each of those moments, there is best practice, and worst practice. When it comes to developing consistent, automated best practice, or excellent technique, why do businesses rely on Coach #1 or Coach #2 for the job of developing leaders?

CEOs see the issue. They say their own corporate Learning & Development programs are not producing readiness, they need to, and the stakes are high.

We are continuing to point at player failure, and we keep investing in the L&D version of the Knowledge Coach and Talking Coach. That won’t cut it.

Conclusion:

1. Leadership development is the #1 HR priority globally.

2. Existing leaders aren’t capable, and hiring new ones isn’t the answer.

3. CEOs know transformation is a requisite for success. But the majority of organizations openly admit their leaders cannot lead.

This is not a resource problem. It’s a structural failure of the development model itself. Leaders cannot lead because they’re not mastering the right fundamentals. Remember what Coach #3 said.

Theory doesn’t work. Talking doesn’t work. What are the fundamentals to master, how do we do it?

Match level instincts are automated by “firing and wiring” neurons with deliberate practice, over and over again, on the edge of your capability.

It’s time to align the rhetoric with the right approach.

I’ve experienced this done well. Engaged, high performing teams are motivating for the people and drives progress for the business. Staff score goals, companies win games. Win-win.

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PEOPLE DON’T KNOW WHAT THEY THINK THEY KNOW.

Before reading this post, download this picture. Follow the directions. See what happens.

Rebecca Lawson assessed people’s functional understanding of a familiar and relatively simple object: the bicycle.

Step 1 - First she asked people how confident they were in their understanding of how a bike works. (1-7 scale)
Step 2 - Then she asked them to complete the partially drawn picture. I love this study because EVERYONE knows what a bike is. The mechanics are in plain view, we’ve got them in the garage, you’ve been riding them since you were 4, you’ve probably seen one today.

I ride bikes, so of course I know how a bike works.

I was stunned and deeply uncomfortable at how I struggled to complete the picture. “Wait…what?” Instead of finishing the exercise, I released the tension by walking around the corner and looking at my bike. So I got there, but I cheated the test. This was ten years ago, it effected me. I felt inadequate when my brain froze up. I had to work at it, it wasn’t automatic, or easy. I experienced a glaring gap between my assumed knowledge and working knowledge and this bears out in Lawson’s data.

FINDINGS:

• Confidence score for working knowledge of a bike – 4.7 on 1-7 scale

• 64% draw a bike that won’t work - it’s inoperable!

• 36% drew the chain around BOTH front and back wheels - impossible physics.

Conclusion - The majority of us think we know, but don’t.

64/100 people will draw a bike that does not work. It’s a predictable failure, but that’s only half the story.

I’ve replicated this dozens of times. It’s remarkable and humbling to witness the emotional journey that people take when they pick up the pencil for a task they know they can do and they face plant.

Sometimes they recognize that they’re not getting it right, other times people are supremely confident all the way to the reveal. Either way, the realization doesn’t quite compute, their brain struggles to internalize the result. They sit in the uncomfortable aftermath, trying to make sense out the mistake and how it feels.

My tests have skewed toward people who ride – the pre-test scores clustered around 5/7 in their confidence in how how a bike works. My respondents also tended toward 40+ year old men who are successful business leaders. Their self image is rooted in competence and capability. They are executives! Their failure rate was the same, roughly 65% drew a bike that wouldn’t work.

These are friends of mine, but even tho it was a friendly context, there was a felt sense of disbelief. “No way.” “Huh, I’m not sure what to say.” “That can’t be true.” “Wait, why is my bike wrong?” “Whoa. Wild.” “Hmm.”

And sometimes they were silent, taking it in.

Regardless of their response, there was a palpable vulnerability that emerged from the wreckage of their presumed understanding of a bike’s basic parts, and how they function.

When I look back to how my blinkered brain, I experienced it as a savage take down. This was supremely dissonant with my self image, and when people feel that kind of mismatch, it registers in their nervous system as a threat.

I call this the Bicycle Problem.

So what’s this have to do with with work?

Everything.

THE BICYCLE PROBLEM IS THE CORPORATE PROBLEM

IT’S WORSE AT WORK.

No One Knows Your Strategy — Not Even Your Top Leaders
– Donald Sull, Charles Sull, and James Yode, MIT Sloan Management Review, 2018

I love this study too, it mirrors my own Six Sigma Project on strategic clarity.

I assumed the execs at the top would be clear on strategy, while the rest of us weren’t getting the message. My hypothesis was that the alignment issue was tiered, but I was wrong.

The MIT study showcases what I found, except their study was huge: 4,012 respondents across 124 companies, with high response rates at each level.

FINDINGS:

• C-Suite that can name top 3 priorities - only 51%.

• VP levels were worse - only 22%.

• Managers were even worse off - 13%.

If you can’t name the priorities, how can you lead for them?

If people are operating with a sketchy understanding of common objects like a bike and fail to explain the working mechanics that are in plain view, we can appreciate how the Bicycle Problem manifests in our work contexts.

Business priorities are in flux, they’re more complex, and understood differently across groups. Executing in this environment is tough.

I can also tell you, from experience, when the CEO’s in the 128 surveyed companies reviewed the findings with their C-suite teams, there were uncomfortable moments.

Despite all the confidence, leader-like affect, and communication skills, half of the top team didn’t know what they thought they knew – and it was in full display. This experience went beyond personal discomfort, this moment registered as a threat to their professional standing.

There’s an emotional life to being wrong when you’re conditioned to succeed by putting on a front for being right.

A friend of mine works at one of the prestigious strategy consulting houses. I asked her what she thought about the MIT finding. Specifically, how people in her firm would respond to it.

And so it goes.

Conclusion - The Bicycle Problem IS the Corporate Problem.

THESE FAILURE MODES ARE HARDWIRED IN INDIVIDUALS

THINKING FAST AND SLOW.

In my early career, I co-founded Good Company, a sustainability consulting firm, and I wanted to answer a simple question: why do people make decisions against their own self interest?

One of our advisors introduced me to the work of Kahneman and Tversky, the decision science pioneers (Michael Lewis wrote about them in The Undoing Project because people kept telling him Kahneman and Tversky were doing “moneyball” science for decades). What struck me wasn’t that humans are biased. We all know that now. It was that our cognitive shortcuts are not occasional glitches. They are the default operating system.

And there’s an big pile of research now that shows all the ways humans get tripped up by biases and heuristics.

SOME TAKE-AWAYS:

• Human cognition defaults to what FEELS right, not accuracy for what’s right.

• It’s hardwired

• Thinking fast is how it shows up, training slow is how to counter it.

My point is not to say our brains trip us up, we know that. My case is that when it comes to work we behave as if leaders are self-aware, calibrated, and structurally rigorous by default. We assume confidence signals competence. We assume experience equals understanding. We assume that if someone can speak about something fluently, they must know it deeply.

We assume people know. They assume they know. And we keep building systems on top of that assumption.

In other words, our corporate operating systems ignores the hardwiring. And it lays the foundation for a work experience that sucks.

Conclusion - If the failure modes are hardwired and grounded in our biology, then the Bicycle Problem failure mode is not a bug, it’s a feature.

Even for smart people.

THE PITFALL OF THINKING IN GROUPS

CONFUSING ACCESS & EXPERTISE.

Sloman and Fernbach explore how outsourcing thinking predates the “cognitive offloading” issues we talk about with AI, but with a twist.

This book explores how the hardwiring for individuals shows up in social groups.

Example they use: ask someone if they know plumbing, the person will run an internal scan on their network, “Do I know a plumber?”

If they do, a magic switch happens and they’re likely to say, “Yes, I know how plumbing works.” If you know someone who has the knowledge, you assume it as your own.

I know plumbers. I’ve tried to fix plumbing. I face planted.

BOOK INSIGHTS:

• Groups magnify the Bicycle Problem because we confuse knowledge access with working knowledge.

• Expertise in our network gets misattributed as our own

• In today’s web-based access to just about anything, confusing access with personal expertise has a wild implication for Dunning–Kruger effect.

This puts a multiplier effect on the bicycle problem.

[I think I know but don’t + I know an expert so that’s also my expertise].

Sloman and Fernback call it a knowledge illusion. These are robust, scientifically grounded findings.

When I’m introducing this science, I slow down and ask:

It sucks.

Why?

This causes a flywheel of confusion that hardens into a culture. The animating factor isn’t a fact case, it’s often the person with the most persuasive opinion. The perpetual mismatch between what people THINK they know, and can actually explain, sets in.

I call this Opinion Land. Most of us are working in Opinion Land.

Conclusion - Opinion Land is the work environment that emerges from a culture of confusion.

THE STATUS GAME (OFFENSE)

IN OPINION LAND, WE PLAY OFFENSE WITH OPINIONS.

In Opinion Land, we play offense with opinions, and the stakes are high because status protects us.

Will Storr, in The Status Game, makes a blunt point: humans are biologically wired to seek status because status equals safety. When our competence is questioned, our nervous system reacts as if something essential is at risk.

That’s why the Bicycle Problem stings.

When an executive draws a bike that doesn’t work, it isn’t just a mistake. The reveal is more than a threat to identity, it’s experienced as physical danger.

Against the backdrop of current events, Storr’s book can feel pretty damn dark. Despite grotesque debasement, he explores in detail how the payoff of status has a remarkable power to bouy people who are suffering. And they go to great lengths to protect the rank.

Some of the themes he covers:

1. STATUS IS BIOLOGICAL: We are wired to protect our standing. Being wrong feels threatening.

2. PRESTIGE > ACCURACY: Organizations reward people who appear competent.

3. OPINIONS BECOME WEAPONS: In status environments, opinions are tools for gaining rank.

4. STATUS THREAT BLOCKS LEARNING: Admitting uncertainty risks losing standing. (More on this later!)

What’s interesting is that Storr argues the impulses for gaining and protecing status are biological.

So we’ll add this new physiological piece:

1. I think I know but don’t (Bicycle Problem)

2. I know an expert so that’s also my expertise (Knowledge Illusion)

3. Seek status for safety (Status Game)

So in Opinion Land, where cultural confusion is set, it’s not the fact case that wins advancement up the hierarchy, it’s confidence, the charisma, and persuasion.

STATUS GAME IMPLICATIONS:

• Success for leaders is scored by the “winning opinion”,

• Their progress is tracked via advancement up the hierarchy.

• Meritocracy gets lost

• Charisma and confidence win out over inquiry and competence

Over time, a culture of confusion develops two value systems - stated and unstated.

The stated system is based on promoted vision, mission, and values. Employee perks, talk of radical impact in the world, and strategic slogans roll in, year after year, wrapped up employee branding programs.

The unstated system is based on unspoken power rules of Opinion Land, people align to it, and the consequences become the “corporate politics” that shape behavior.

Don’t think → signal alignment
Don’t solve → stay politically safe
Don’t question → mirror relationships
Don’t experiment → follow the loudest voice.

Over years of conditioning in Opinion land, we see who wins and who doesn’t, we see when they win and when they don’t, and we see a pattern of power emerge. It’s a training mechanism that reinforces fear, erodes trust, and increases anxiety. That sucks.

Conclusion: In Opinion Land offense we play for status by (1) advocating for the opinion case that advances you, or (2) aligning to leader opinions that keep you safe.

THE STATUS GAME (DEFENSE)

IN OPINION LAND, WE PLAY DEFENSE BY ELIMINATING THREATS

The flipside of advancing opinions is protecting your image from threats. This is how defense is played in Opinion Land.

Robert Kegan argues that many adults operate from what he calls the socialized mind, an identity shaped by external validation. Success, approval, credibility, and belonging become fused with the self. In Opinion Land, opinions are identity, and in the AI world of TOO MUCH, it goes beyond dysfunction. It breaks.

Here’s how.

In the Kegan world where we’re in over our heads, when someone questions your idea, it doesn’t register as collaborative refinement. It registers as a threat to who you are as a competent leader. Under threat, biology takes over. Defend. Deflect. Double down. That leads to a dysfunctional work environment where we’re being ground down by the dissonance between the two power systems. It sucks.

In AI’s TOO MUCH world, opinions cannot organize reality fast enough. Alignment thru agreement cannot substitute for understanding. Waiting for certainty becomes lethal. The very behavior required for progress — slowing down, inviting challenge, co-constructing understanding — becomes the behavior that feels most dangerous. Collaboration collapses not because people don’t value the benefits that show up when it’s done well. But because they’re protecting face as a mode for survival. It breaks.

Leaders are “in over their heads”, facing the pace, pressure, and novelty of AI. The demands exceed their current way of making meaning. Microshocks accumulate, the nervous system shifts to survival mode, the prefrontal cortex goes offline. They default to protection and look to eliminate the threat.

This is the AI overwhelm.

Biology takes over. We don’t rise to the occasion, we sink to our training, and the systems of Opinion Land have trained us to protect our status, not test assumptions.

But you can’t eliminate AI. The pressure isn’t optional. There’s no escaping.

It breaks.

When adaptation speed falls behind the environmental speed, collapse follows.

This gap will trigger a mass extinction event for businesses that cannot upgrade how humans think under pressure.

It is also a moneyball moment for those that can. The Human Embodiment Problem is not about intelligence. It’s about wiring interacting with hierarchy under pressure.

Until we address that directly, no AI strategy will scale.

Where does that leave us?

Here’s a recap of the Human Embodiment Problem.

• Individually, we overestimate what we know.

• Socially, we amplify that illusion.

• Hierarchically, we reward confident opinion.

• Under threat, we defend identity.

• Under AI-scale pressure, that system breaks.

To wrap this post, consider this refrain that’s circulating these days “Every company will become an AI company—or it will not be a company.”

The real question isn’t whether AI will scale. It’s whether humans will.

Conclusion - To close the people readiness gap, the Human Embodiment Problem has to be addressed directly.

• Predictable failure mode: People think they know, but don’t.

• Culture of Confusion → Opinion Land: Individually hardwired, socially amplified.

• Status Game offense & defense: Power organizes around opinions and politcs.

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We’re getting explanations of AI transformation — not predictive guidance on what to actually do.

Check out these quotes, and then ask yourself: how would I lead my team to act on it?

I feel uncomfortable when I read these quotes. They’re not actionable.

Don’t we need to know what we are changing from? And what are we changing to?

There is tension between the rhetoric and reality. I see these tensions all over. Here are a few:

It puts pressure on leaders. What do we do about it?

Let’s take a look at Brynjolfsson, Korinek, Agrawal ‘s paper. I think it illustrates a core issue that runs underneath the tensions operators experience: we’re getting explanations of how things are, but not predictive solutions that give us confidence in what to actually do.

In the working paper they define “transformative” precisely.

Transformative AI as something that changes the growth regime, not just tools or tasks.

Per the quote, the 3-5x productivity is achieved by changing production functions, institutions, and decision-making structures.

They warn about a core challenge - pace. The paper identifies a structural speed mismatch between AI acceleration and institutional adaptation.

Organizations, institutions, and skills evolve far more slowly than technology itself, creating major transition risks.

Defensible, but not actionable.

And they suggest a solution – scenario planning over forecasting.

Scenario planning explores plausible futures rather than predicting a single outcome, given high uncertainty and nonlinear dynamics.

This is essentially a concession that explanatory models fail under transformational change conditions. If your AI strategy explains the past better than it predicts the next decision, it’s already obsolete. So scenario planning is a valuable tool.

Then they list “Meaning and Well-being” as a grand challenge, not a solved one:

What provides meaning and motivation when traditional work and labor structures are disrupted by AI?

After 24 pages, they still frame meaning, engagement, and well-being as open research questions — not operational levers.

This isn’t wrong, but the flip in the paper showcases the employee engagement issue as an open RESEARCH problem.

Their implied logic seems to be this: if engagement were actually understood, it wouldn’t still be an open research problem.

Here’s another example. Take Julie Sweet’s comments.

Let’s dissect the framing:

1. AI is reshaping work.

2. We need new skills. We will up-skill.

3. Some employees cannot be re-skilled fast enough – we will exit them.

4. We will bring change management to clients to help them navigate this.

5. And we are cutting 11,000 people in this process.

On the surface, that sounds coherent. But structurally, there’s tension. If “change management” worked predictively, then:

1. Engagement metrics would show sustained improvement over the last decade. They don’t. Leadership capability would have improved, it hasn’t.

2. Re-skilling success rates would be demonstrably high.

3. Performance under transformation would be measurable and contracted.

Instead, what we see across the industry:

1. 10+ years of leadership investment.

2. 10+ years of engagement stagnation.

3. Repeated restructuring cycles.

4. Re-skilling + layoffs occurring simultaneously.

That suggests the model is reactive, not predictive. It explains turbulence after the fact. It looks like an old playbook, new language.

Accenture frames its restructuring around up-skilling for the AI era.
It’s claiming “change management” for AI as a core competence.
What’s different this time?

The results are starting to show up, corporate value is lagging technology. The perception appears to be tipping toward client frustration, which isn’t helping operators know what to do. The stories around the disappointment in perceived value for management consultants would be entertaining if the stakes weren’t so high, for all of us.

So where do we go from here?

As the Brynjolfsson, Korinek, and Agrawal paper makes clear, after dozens of pages and nine “grand challenges,” the human variable remains an open question.

That’s the tell.

Conclusion - We’re attempting system-level transformation without a predictive model of how humans actually operate under pace, pressure, and novelty.

Transformation ≠ tools.
Explanation ≠ prediction.

And without prediction, progress stalls. Engagement stagnates. Restructuring cycles repeat. AI advances technically while organizations bottleneck operationally.

The constraint isn’t the technology.

It’s human readiness.

I call this the human embodiment problem. It’s hard-wired individually, amplified socially, and sitting directly in the path of change.

This post is diagnostic. Next, we explore the root our explanatory reflex and why its so pervasive at work.

THEN, we’ll get into what we do about it.

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We’ll apply them specifically throughout this substack.

PRINCIPLE #1: KEEP IT SAFE

Feeling safe and in control while solving problems and making decisions is a core human need.

1. What’s required for professionals to feel safe and in control when solving problems or making decisions?

This is true for children, this is true for adults. When that sense of control slips under the pressure of uncertainty, thinking narrows. Self preservation impulses take over, people become reactive. “Watch out! Protect yourself.” The nervous system shifts into survival mode. Teams default to leader opinions, old playbooks, or silence. Old habits take over. These are physiological processes, they’re foundational.

Most of us aspire to feel calm, clear, and capable under pressure. The gap between that aspiration and daily experience is where much of the strain in modern work lives.

AI is challenging this core human need in new, unprecedented ways.

PRINCIPLE #2: DON’T OUTSOURCE YOUR MIND

Problem solving and decision making should not be outsourced to machines.

2. If problem solving and decision making are so central to performance, what trends are making them harder to do well?

Solving problems and making decisions are fundamental human skills that allow us to navigate our environment. Our capacity to do them well predicts performance, judgment, and adaptability.

If we give these up, what is left?

Today, many people feel less confident using them, not more. As the context for decisions and problem solving becomes more complex, the impulse is increasingly to hand the work over to AI.

The cost isn’t abstract. When people rely on machines to think for them, confidence erodes, decision quality degrades, and orientation is lost.

At scale in organizations, this shows up as slower execution even as everything speeds up. When judgment isn’t exercised, the mental muscles required to use it atrophy.

At scale in young people, it shows up earlier: the cognitive “muscle” needed to navigate hard problems or decisions never fully develops.

In both cases, the same thing is lost — a felt sense of control.

PRINCIPLE #3: SKILLS OVER AWARENESS

Professional capacity is built through deliberate skill acquisition, not information exposure.

3. Where are we assuming that awareness will translate into professional capability?

We talk about skills constantly. Leadership, critical thinking, adaptability. But knowing about skills doesn’t mean someone can use them when pressure hits.

Most of us sense this intuitively. The frustration comes from not knowing what actually builds capacity, especially when the environment won’t slow down to accommodate learning and development.

PRINCIPLE #4: PLAY TO BUILD

Game-based learning is more fun, fast, and effective.

4. What model of skill acquisition is being used — traditional learning, gamification, or game-based learning?

Traditional learning is passive. It transfers information, but it doesn’t reliably build skill. Gamification often remains passive as well, adding game mechanics as an overlay while leaving the underlying learning model unchanged.

Game-based learning is different. The game is the learning. Skills are acquired through goal-driven practice with immediate feedback. Neural wiring happens faster, with fewer wasted repetitions. The advantage of active learning over passive learning isn’t marginal, it’s structural.

Skill acquisition is most effective when development is designed around repetitions at the edge of capability, progress is visible, and feedback is fast. It still requires effort. But when learning is game-based, fewer high-quality reps produce better outcomes, Momentum builds naturally. Capability compounds instead of stalling.

PRINCIPLE #5: TRAIN FOR TURBULENCE

Pace, pressure, and novelty are here to stay, we have to up-level our ability to handle it.

5. In this AI-accelerated world, does the “safety” strategy aim to avoid the pressure, or handle the pressure?

Safety in an AI-accelerated world doesn’t come from avoiding turbulence. It comes from building the capacity to operate inside it.

Avoiding pressure isn’t realistic. AI velocity is not going anywhere. Operating without a way to handle that pressure takes a toll.

There’s a difference between chaos and challenge, the line between them is often capability.

When confidence is grounded in competence, the same conditions feel fundamentally different. Cognition stays online. Judgment improves. Outcomes change.

In the end, winners and losers won’t be determined by who faced less pressure, but by who was more deliberate about developing the capacity to handle it.

#6 PRINCIPLE: STRUCTURE CREATES CLARITY

Clear thinking doesn’t happen by accident; it requires shared structure.

6. Does the culture explicitly help hardworking people think clearly – as individuals AND as groups?

Intelligence and effort are abundant. Structure is not.

Without common ways to frame problems, test assumptions, and navigate decisions, even strong teams drift.

The aspiration is clarity. The lived experience is often a confusion.

When confusion becomes the background condition of work, it solidifies as culture. Subtle, persistent, and rarely examined, a culture of confusion sets the logic for how things get done. But over time it slows progress, confidence erodes, and staff are condition to align to leaders who operate from status protection. If status is challenged, it provokes threat-based reactions, which cause cascades of symptoms that we recognize as “corporate politics”, power struggles, personality conflict, and a reliance on formal authority.

Principle #7: Speed and rigor are twin requirements going forward, you need them both.

7. Are speed vs rigor seen as a tradeoff, or a source of competitive advantage?

Many assume speed and rigor are tradeoffs, like an axiom where you get one or the other, or a medium amount of both. That’s an old paradigm.

In the modern work context, speed and rigor are twin requirements, not a tradeoff.

Modern work seems to demand both immediacy and quality. But we often act as if we must choose and compromise. Or worse, we act as if we should have both, but don’t develop the capability to work with speed and rigor. None of this feels sustainable.

When that tension goes unresolved, people rush and regret it, or slow down and feel exposed, or assume they have them both but don’t.

Game On AI starts here.
Not with tools or answers, but with a shared exploration of what’s really at play and why it feels the way it does.

These questions kept showing up in my leadership conversations, in teams under pressure, in my own attempts to stay oriented. The questions don’t age quickly because they’re grounded in how humans actually work.

If this feels familiar, let me know. And if you want to explore them more deeply, this is where I’ll be working them through carefully, practically, and in the open.

MY THESIS for organizations in AI world: the primary performance constraint isn’t technology. It’s human capability under pressure.

MY MISSION is to help people, teams, and organizations develop the structured thinking, speed, and rigor needed to feel competent navigating the times we’re in.

I HAVE 4 CORE BELIEFS,

This is the “sandbox” boundary.

Here’s a closer look.

Belief #1: Work sucks, and it shouldn’t

The prevailing felt sense that “work sucks”... it’s not precise.

It’s a shorthand for a cluster of symptoms people struggle to name:

• effort without traction,

• motion without progress,

• pressure without clarity.

When these conditions persist, motivation drains.

Engagement, which I use as a directional proxy for high performance, erodes. Productivity declines. Not because people don’t care, but because the system makes it hard to feel effective.

This pattern isn’t new, and it isn’t misunderstood.

Researchers have measured it. Experts have explained it. Entire industries have formed around fixing it. We know that organizations with high engagement outperform their peers by a wide margin. (23% more profit!)

And yet, the trend has continued in the wrong direction.

Why?

Even societies with high overall life satisfaction show only modest work engagement, suggesting that the problem isn’t cultural happiness, but how modern work is designed and led.

Work shouldn’t feel this way. But across the globe, people consistently report that it does.

Engagement is a global issue. Engagement scores have flattened. And in some regions, like the US, scores have reversed.

Why?

Gallup’s same research estimates 70% of the variance in team engagement is attributable to the manager. This is the claim today, and it was also Gallup’s finding in 2017. They even wrote a book about it in 2019. If engagement is largely driven by leadership, which makes sense to me, then disengagement isn’t a morale problem, it’s a capability problem.

And the benefits of staff engagement aren’t trivial. Profit is 21-23% higher when organizations have 70% engaged workforce. That’s the sacred metric at the heart of capitalism. It’s the ultimate measuring stick for CEO success.

Or seen the other way, if work sucks for the people doing it, the profits are lower, and CEOs don’t measure up.

So…

IF we know that “Engagement”…

• drives profits (core business objective)

• reflects well on leadership (profits are ultimate success measure for leaders)

• reflects well on a motivated workforce (people like work)

• AND the source is known (we know it’s the manager)

THEN…

• that’s a straight ahead solution (develop your leaders)

So read the book, everyone, and you’ll get those benefits. Right?
Nope. So why is it getting worse, not better?

Check the three quotes from McKinsey, they track a 12-year arc of thought leadership.

• Leadership development gap is identified (Uh… that’s a consulting opportunity!)

• 90%+ CEOs say they’re investing but see no impact (Where’s the solution?)

• There aren’t enough good leaders. (So why aren’t there more?)

The last quote is bewildering to me. The insinuation is subtle but the underlying assumptions are there: the problem is leaders aren’t good enough, if leaders read our book they’ll see how to invest in themselves, and then they’ll be amazing. That’s how they’re promoting their book, which is full of advice that doesn’t appear to move the engagement<>profit needle. At least I’m not seeing the book promoted as a predictive solution for a 70% engaged work force that will drive 23% more profit to your bottom line.

McKinsey and Gallup are not alone – the experts are working with explanatory solutions, but according to the data, not predictive ones.

If anyone has heard that experts are confident in the predictive power of their advisory services, are they doing performance contracting arrangement tied to the causal relationship between their solution’s ability to secure engagement, then profits? How about AI transformation services, are those tied to performance contracts? (More on this in later posts.)

My conclusion is this: if they had it, they’d use it, and promote it everywhere.

CEOs with low engagement want want 23% more profit. CEOs want to see an ROI on their L&D programs’ ability to prepare leaders for AI’s transformational shocks we’re all experiencing. For reference, M&A decisions for cost synergies are pinned on expectations for less than 23% benefit.

So what needs to be true for us to be seeing what we’re seeing?

Whatever the consulting experts are selling - change management, leadership IQ, AI transformation - it has explanatory neatness. But it isn’t working.

Work still sucks, according to the humans who get surveyed, and AI is going to make a lot worse.

Belief #2: AI will make it a lot worse.

There is a mismatch between the demands of our work environment and our ability to handle it. It’s turbulent.

The AI world has accelerated this mismatch, it’s been welcomed with the promise of productivity, but it’s also arrived with too much pace, pressure, and novelty, all at once.

It’s TOO MUCH.

There are a lot of risks to the TOO MUCH, the risks are rooted our bodies, and the solutions are too.

BELIEF #3: Biology explains failure modes

Human responses to these TOO MUCH conditions are rooted in biology, thinking, and physiology.

Without a structured way to respond to the TOO MUCH, confusion sets in, the amygdala gets hijacked, cognition collapses, and predictable failure modes show up.

But cognitive collapse is avoidable.

If AI work conditions exceed human capacity, and AI is here to stay, the only option is to increase the capacity side of this equation, the human side.

A good coach for a skill-based sport will tell you, “game day capability is determined by the mastery of key fundamentals, my job is to move them up the mastery curve of those fundamentals.”

According to what CEOs are saying, when it comes to developing consistent, automated best practices, corporations don’t seem to have landed on a predictive set of “fundamentals” to fire and wire.

The primary differentiator isn’t technology, it’s the human capability needed to make decisions and solve problems with BOTH speed and rigor.

Turbulence forces action. Action defaults to what is wired. If what’s wired is outdated, failure is predictable.

This is not a moral failure – the individual’s fault.

It’s a capability developmental failure.

BELIEF #4: HUMAN CAPABILITY WINS

The critical question each organization needs to answer is this: “How do we know we are developing the team capacity to thrive in AI pace, pressure, and novelty?

Leaders, like Microsoft’s CEO Satya Nadella, recognize the constraint with AI isn’t the technology, it’s how how well, and how fast, humans change how they work.

Effective development of these fundamentals will have a non-linear benefit. Since AI is the commodity that everyone has access to, then the differentiator is capability development.

Like any game, the capacity to solve problems and make decisions, without a tradeoff between speed and rigor, will determine winners and losers.

This will happen fast.

These beliefs framed my point of view, but they weren’t actionable.

They raised questions, and over time the useful questions helped me convert what I was seeing into clarity or action.

I also started to notice these top questions had a principle that underpinned it.

In classic “first principles” thinking you look for “the first basis from which a thing is known”.

For me, these principles are more like “the first basis from which to explore what should be known.”

The emphasis isn’t semantic, it’s practical. One key risk for human cognition is this: when we think we know, we stop asking if what we think is actually true.

Game On emerged from that exploration. The questions became lenses I could use to understand things, or change things.

Head to post 2 for a closer look.

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This work started with a simple question: For decades experts have explained why work is broken – so why does it still feel broken?

I saw the same pattern showing up when (smart) people were under pressure.

Over and over, it felt like problem solving and decision making were crashing in individuals and teams. And throwing “best practice” at the issue didn’t change it. I tested that. I was missing something, I wanted to know what it was, I went to work on it.

1. What I learned, and why it matters.

I saw it across roles, industries, and situations. Different teams. Different industries. Different processes. Same failure mode. What I found was not a motivation problem. Not a strategy problem. Not an alignment problem. Not an AI problem.

It was a capability problem.

Most of the pain ahead — in organizations, markets, even careers — won’t come from lack of tools. It will come from degraded thinking and decision making under pressure.

I built structured methods to counter that.

2. Who am I?

I’m an operator. I’ve spent 25+ years leading growth, transformation, and new-market entry in high-uncertainty environments.

The best practices I’d relied on weren’t delivering the results I expected, and the solutions I was being sold weren’t addressing the patterns I kept seeing. I wanted solutions backed by research and reliable in live environments.

So I built them.

I’ve invested 10,000+ hours refining how problems are framed and decisions are made under pressure. I use them daily. They work.

Now I’m putting them in the open.

3. Who is this for?

• Individuals who want to level up their own capacity

• Leaders who want want your teams to execute with BOTH speed and rigor

• Educators who want their students to be ready for the AI work environment

• Investors who want A-team performance in their portfolio companies

• L&D Leaders who feel pressure to deliver ROI and leader readiness

4. This is a conversation.

If you’re wrestling with cognitive bottlenecks, execution drag, or decision fatigue, I want to hear what you’re seeing.

I’m looking to share notes with professionals, educators, and leaders who are ready to move on from old playbooks that sound reassuring but reproduce the same results.

5. This Substack is organized around…

This Substack isn’t commentary. It’s structured work.

• 1 mission

• 4 core beliefs

• 7 recurring questions (principles)

Posts #1-#4 posts are foundational by design. We explore the roots of the problem.

Post #5 and beyond moves into application. What to do, how to do it. Decoding real-time developments will be a recurring theme.

Lets get to it.

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